class_docstr: This similarity function performs a bilinear transformation of the two input vectors.  This
  function has a matrix of weights ``W`` and a bias ``b``, and the similarity between two vectors
  ``x`` and ``y`` is computed as ``x^T W y + b``.
  
  Parameters
  ----------
  tensor_1_dim : ``int``
      The dimension of the first tensor, ``x``, described above.  This is ``x.size()[-1]`` - the
      length of the vector that will go into the similarity computation.  We need this so we can
      build the weight matrix correctly.
  tensor_2_dim : ``int``
      The dimension of the second tensor, ``y``, described above.  This is ``y.size()[-1]`` - the
      length of the vector that will go into the similarity computation.  We need this so we can
      build the weight matrix correctly.
  activation : ``Activation``, optional (default=linear (i.e. no activation))
      An activation function applied after the ``x^T W y + b`` calculation.  Default is no
      activation.
code: def reset_parameters(self):
      torch.nn.init.xavier_uniform_(self._weight_matrix)
      self._bias.data.fill_(0)
  
file_path: magnitude/pymagnitude/third_party/allennlp/modules/similarity_functions/bilinear.py
imports: from __future__ import absolute_import
  import torch
  from torch.nn.parameter import Parameter
  from allennlp.modules.similarity_functions.similarity_function import SimilarityFunction
  from allennlp.nn import Activation
label_desc: Use image node_id 2 for calling the BilinearSimilarity obj's underlying member method code with example usage: obj.reset_parameters() without return types